{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import subprocess\n",
    "import time\n",
    "import os \n",
    "import sys \n",
    "import io\n",
    "import functions\n",
    "import importlib\n",
    "import inspect\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: numpy==1.26.4 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from -r ../requirements.txt (line 2)) (1.26.4)\n",
      "Requirement already satisfied: pyfixest==0.20.0 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from -r ../requirements.txt (line 3)) (0.20.0)\n",
      "Requirement already satisfied: statsmodels==0.14.2 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from -r ../requirements.txt (line 4)) (0.14.2)\n",
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      "Requirement already satisfied: plotly==5.21.0 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from -r ../requirements.txt (line 7)) (5.21.0)\n",
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      "Requirement already satisfied: pillow>=8 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r ../requirements.txt (line 15)) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn==0.13.2->-r ../requirements.txt (line 15)) (3.1.2)\n",
      "Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from numba>=0.58.0->pyfixest==0.20.0->-r ../requirements.txt (line 3)) (0.42.0)\n",
      "Requirement already satisfied: six in /home/zach/miniconda3/envs/replication_test_apsr/lib/python3.12/site-packages (from patsy>=0.5.6->statsmodels==0.14.2->-r ../requirements.txt (line 4)) (1.16.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args='pip install -r ../requirements.txt', returncode=0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# install requirements\n",
    "\n",
    "subprocess.run(\"pip install -r ../requirements.txt\", shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['acf', 'add_constant', 'add_values', 'adfuller', 'ar_select_order', 'confusion_matrix', 'create_FE_data', 'create_TS_data', 'create_VAR_model', 'etable', 'feols', 'fepois', 'fit_asinh_linear', 'fit_log_linear', 'fit_natural_linear', 'fit_poisson_FE', 'get_data', 'get_granger', 'granger_table', 'latex_table_FE_models', 'make_attention_plot', 'make_mip_plot', 'make_subplots', 'mip_data', 'pacf', 'plot_irf', 'print_MIP_descriptive_stats', 'print_confusion_matrix', 'print_twitter_descriptive_stats', 'return_mip_data', 'variance_inflation_factor']\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(functions)\n",
    "from functions import *\n",
    "\n",
    "# Get all functions in the module\n",
    "functions_list = [f[0] for f in inspect.getmembers(functions, inspect.isfunction)]\n",
    "print(functions_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class config: \n",
    "    # Data\n",
    "    data_path = '../data/'\n",
    "    pooled_data = '../data/pooled_data.csv'\n",
    "    individual_data = '../data/individual_data.csv'\n",
    "    confusion_matrix = '../data/confusion_matrix_data.csv'\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixed effects estimation \n",
    "Fixed effects estimation uses a dataset indexed by legislator, time and issue. The MIP (public opinion) column is matched by date and issue and is repeated for each legislator.\n",
    "\n",
    "The function returns two datasets - the first is the US, the second is the UK. Each have the same format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "us, uk = create_FE_data('../data/individual_data.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fixed effects estimation follows the same template for each country. The only difference is the data used. The first model uses the log value of the attention, the second uses an inverse hyperbolic sine transformation of the number of issue tweets. The third model uses the raw value of the number of issue tweets (without any transformation). There are functions for each of these models.\n",
    "\n",
    "1. `fit_log_linear` - fits the log-linear model with fixed effects for legislator, date, issue and party (party is abosorbed in the fixed effects).\n",
    "2. `fit_asinh_linear` - fits the inverse hyperbolic sine transformation model with fixed effects for legislator, date, issue and party.\n",
    "3. `fit_natural_linear` - fits the raw value model with fixed effects for legislator, date, issue and party.\n",
    "\n",
    "Each of the three functions take two arguments:\n",
    "- `data` - the dataset to be used for the estimation\n",
    "- `gender` - the gender of the public opinion data to be used \n",
    "- `cov` - the number of cluster variables (either 2 for time*legislator or 1 for time)\n",
    "\n",
    "\n",
    "Additionally, there's a function that combines the models and outputs the results into a latex table for the article. This function is called `latex_table_FE` and takes an `etable` of the models as input (an [`etable`](https://s3alfisc.github.io/pyfixest/reference/report.etable.html) is a method from the [`pyfixest`](https://s3alfisc.github.io/pyfixest/pyfixest.html) library that combines multiple models into a single dataframe)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Materials G\n",
    "\n",
    "### Table A11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllll}\n",
      "\\toprule\n",
      " & est1 & est2 & est3 & est4 & est5 & est6 \\\\\n",
      "\\midrule\n",
      "depvar & Tweets & log(Attention) & np.arcsinh(Tweets) & Tweets & log(Attention) & np.arcsinh(Tweets) \\\\\n",
      "Women's Salience & 0.254*** (0.072) & 0.117*** (0.024) & 0.080*** (0.019) & 0.641*** (0.068) & 0.308*** (0.027) & 0.206*** (0.018) \\\\\n",
      "Vote Share & -0.774** (0.283) & -0.341 (0.292) & -0.227* (0.099) & 0.269 (0.322) & -0.028 (0.443) & 0.089 (0.125) \\\\\n",
      "Total Tweets & 0.034*** (0.001) &  & 0.009*** (0.000) & 0.042*** (0.001) &  & 0.014*** (0.001) \\\\\n",
      "Women's Salience:Women Representative & 0.370*** (0.097) & 0.082*** (0.022) & 0.082*** (0.021) & 0.169*** (0.042) & 0.094*** (0.016) & 0.065*** (0.012) \\\\\n",
      "Rep. & x & x & x & x & x & x \\\\\n",
      "party & x & x & x & x & x & x \\\\\n",
      "issue & x & x & x & x & x & x \\\\\n",
      "Time & x & x & x & x & x & x \\\\\n",
      "R2 & 0.305 & 0.182 & 0.349 & 0.266 & 0.206 & 0.349 \\\\\n",
      "S.E. type & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. \\\\\n",
      "Observations & 545848 & 545848 & 545848 & 744425 & 744425 & 744425 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "us_model = fit_natural_linear(us, 'female', 2)\n",
    "uk_model = fit_natural_linear(uk, 'female', 2)\n",
    "us_lin_model = fit_log_linear(us, 'female', 2)\n",
    "uk_lin_model = fit_log_linear(uk, 'female', 2)\n",
    "us_asinh_model = fit_asinh_linear(us, 'female', 2)\n",
    "uk_asinh_model = fit_asinh_linear(uk, 'female', 2)\n",
    "\n",
    "table_g = etable([us_model, us_lin_model, us_asinh_model, uk_model, uk_lin_model, uk_asinh_model], type= 'df')\n",
    "\n",
    "latex_table_FE_models(table_g)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Materials H\n",
    "\n",
    "### Table A12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllll}\n",
      "\\toprule\n",
      " & est1 & est2 & est3 & est4 & est5 & est6 \\\\\n",
      "\\midrule\n",
      "depvar & Tweets & log(Attention) & np.arcsinh(Tweets) & Tweets & log(Attention) & np.arcsinh(Tweets) \\\\\n",
      "Men's Salience & 0.075 (0.063) & 0.057* (0.023) & 0.032 (0.017) & 0.817*** (0.089) & 0.395*** (0.035) & 0.260*** (0.024) \\\\\n",
      "Vote Share & -0.779** (0.284) & -0.344 (0.293) & -0.229* (0.099) & 0.271 (0.322) & -0.027 (0.443) & 0.090 (0.125) \\\\\n",
      "Total Tweets & 0.034*** (0.001) &  & 0.009*** (0.000) & 0.042*** (0.001) &  & 0.014*** (0.001) \\\\\n",
      "Men's Salience:Women Representative & 0.282** (0.086) & 0.053* (0.021) & 0.054** (0.019) & 0.158** (0.052) & 0.085*** (0.020) & 0.061*** (0.014) \\\\\n",
      "Rep. & x & x & x & x & x & x \\\\\n",
      "party & x & x & x & x & x & x \\\\\n",
      "issue & x & x & x & x & x & x \\\\\n",
      "Time & x & x & x & x & x & x \\\\\n",
      "R2 & 0.304 & 0.181 & 0.347 & 0.266 & 0.206 & 0.349 \\\\\n",
      "S.E. type & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. & by: Time+Rep. \\\\\n",
      "Observations & 545848 & 545848 & 545848 & 744425 & 744425 & 744425 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "us_lin_model = fit_log_linear(us, 'male', 2)\n",
    "uk_lin_model = fit_log_linear(uk, 'male', 2)\n",
    "us_asinh_model = fit_asinh_linear(us, 'male', 2)\n",
    "uk_asinh_model = fit_asinh_linear(uk, 'male', 2)\n",
    "us_model = fit_natural_linear(us, 'male', 2)\n",
    "uk_model = fit_natural_linear(uk, 'male', 2)\n",
    "\n",
    "table_h = etable([us_model, us_lin_model, us_asinh_model, uk_model, uk_lin_model, uk_asinh_model], type= 'df')\n",
    "\n",
    "latex_table_FE_models(table_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Materials I\n",
    "\n",
    "This table presents results from a robustness check that subsets the data by only Republicans and Labour Party MPs. The logic behind this test is further articulated in the article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset the data to only include the two parties\n",
    "\n",
    "uk_labour = uk.loc[uk.party == 'Labour']\n",
    "us_rep = us.loc[us.party == 'Republican']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table A13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllll}\n",
      "\\toprule\n",
      " & est1 & est2 & est3 & est4 & est5 & est6 \\\\\n",
      "\\midrule\n",
      "depvar & Tweets & log(Attention) & np.arcsinh(Tweets) & Tweets & log(Attention) & np.arcsinh(Tweets) \\\\\n",
      "Women's Salience & 0.245*** (0.063) & 0.108*** (0.024) & 0.074*** (0.018) & 0.881*** (0.071) & 0.365*** (0.026) & 0.272*** (0.019) \\\\\n",
      "Vote Share & -0.375** (0.126) & 0.177** (0.053) & -0.043 (0.040) & 0.432* (0.188) & 5.145*** (0.253) & 1.351*** (0.086) \\\\\n",
      "Total Tweets & 0.031*** (0.001) &  & 0.009*** (0.000) & 0.044*** (0.001) &  & 0.014*** (0.000) \\\\\n",
      "Women's Salience:Women Representative & 0.265*** (0.040) & 0.055*** (0.011) & 0.047*** (0.008) & 0.127*** (0.015) & 0.058*** (0.007) & 0.045*** (0.005) \\\\\n",
      "Rep. & x & x & x & x & x & x \\\\\n",
      "party & x & x & x & x & x & x \\\\\n",
      "issue & x & x & x & x & x & x \\\\\n",
      "Time & x & x & x & x & x & x \\\\\n",
      "R2 & 0.275 & 0.159 & 0.307 & 0.302 & 0.251 & 0.390 \\\\\n",
      "S.E. type & by: Time & by: Time & by: Time & by: Time & by: Time & by: Time \\\\\n",
      "Observations & 257178 & 257178 & 257178 & 233996 & 233996 & 233996 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "us_lin_model = fit_log_linear(us_rep, 'female', 1)\n",
    "uk_lin_model = fit_log_linear(uk_labour, 'female', 1)\n",
    "us_asinh_model = fit_asinh_linear(us_rep, 'female', 1)\n",
    "uk_asinh_model = fit_asinh_linear(uk_labour, 'female', 1)\n",
    "us_model = fit_natural_linear(us_rep, 'female', 1)\n",
    "uk_model = fit_natural_linear(uk_labour, 'female', 1)\n",
    "\n",
    "table_I = etable([us_model, us_lin_model, us_asinh_model, uk_model, uk_lin_model, uk_asinh_model], type= 'df')\n",
    "\n",
    "\n",
    "latex_table_FE_models(table_I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table A14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllll}\n",
      "\\toprule\n",
      " & est1 & est2 & est3 & est4 & est5 & est6 \\\\\n",
      "\\midrule\n",
      "depvar & Tweets & log(Attention) & np.arcsinh(Tweets) & Tweets & log(Attention) & np.arcsinh(Tweets) \\\\\n",
      "Men's Salience & 0.088 (0.054) & 0.061** (0.023) & 0.035* (0.017) & 1.088*** (0.095) & 0.459*** (0.035) & 0.335*** (0.025) \\\\\n",
      "Vote Share & -0.376** (0.126) & 0.177** (0.053) & -0.042 (0.040) & 0.436* (0.190) & 5.145*** (0.254) & 1.352*** (0.086) \\\\\n",
      "Total Tweets & 0.031*** (0.001) &  & 0.009*** (0.000) & 0.044*** (0.001) &  & 0.014*** (0.000) \\\\\n",
      "Men's Salience:Women Representative & 0.257*** (0.037) & 0.039*** (0.011) & 0.033*** (0.007) & 0.095*** (0.018) & 0.043*** (0.008) & 0.035*** (0.005) \\\\\n",
      "Rep. & x & x & x & x & x & x \\\\\n",
      "party & x & x & x & x & x & x \\\\\n",
      "issue & x & x & x & x & x & x \\\\\n",
      "Time & x & x & x & x & x & x \\\\\n",
      "R2 & 0.274 & 0.159 & 0.307 & 0.301 & 0.251 & 0.389 \\\\\n",
      "S.E. type & by: Time & by: Time & by: Time & by: Time & by: Time & by: Time \\\\\n",
      "Observations & 257178 & 257178 & 257178 & 233996 & 233996 & 233996 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "us_lin_model = fit_log_linear(us_rep, 'male', 1)\n",
    "uk_lin_model = fit_log_linear(uk_labour, 'male', 1)\n",
    "us_asinh_model = fit_asinh_linear(us_rep, 'male', 1)\n",
    "uk_asinh_model = fit_asinh_linear(uk_labour, 'male', 1)\n",
    "us_model = fit_natural_linear(us_rep, 'male', 1)\n",
    "uk_model = fit_natural_linear(uk_labour, 'male', 1)\n",
    "\n",
    "table_I = etable([us_model, us_lin_model, us_asinh_model, uk_model, uk_lin_model, uk_asinh_model], type= 'df')\n",
    "\n",
    "\n",
    "latex_table_FE_models(table_I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix J Poisson FEs \n",
    "\n",
    "### Table A15\n",
    "\n",
    "**Note:** `Pyfixest` is an immature library and does not seem to want to estimate Poisson models with fixed effects. I use the more mature version of [`fixest`](https://lrberge.github.io/fixest/) in R to estimate the Poisson model with fixed effects. The script is called `poisson_estimation.R` and is located in the `code` folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "NOTE: 32,760 observations removed because of NA values (RHS: 32,760).\n",
      "The variable 'gender1' has been removed because of collinearity (see $collin.var).\n",
      "NOTE: 32,760 observations removed because of NA values (RHS: 32,760).\n",
      "The variable 'gender1' has been removed because of collinearity (see $collin.var).\n",
      "NOTE: 9,984 observations removed because of NA values (RHS: 9,984).\n",
      "The variable 'gender1' has been removed because of collinearity (see $collin.var).\n",
      "NOTE: 9,984 observations removed because of NA values (RHS: 9,984).\n",
      "The variable 'gender1' has been removed because of collinearity (see $collin.var).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begingroup\n",
      "\\centering\n",
      "\\begin{tabular}{lcccc}\n",
      "   \\tabularnewline \\midrule \\midrule\n",
      "   Dependent Variable: & \\multicolumn{4}{c}{Issue Tweets}\\\\\n",
      "   Model:                                & (1)            & (2)            & (3)            & (4)\\\\  \n",
      "   \\midrule\n",
      "   \\emph{Variables}\\\\\n",
      "   Men's Salience                        & 0.3218$^{***}$ &                & 0.5899$^{***}$ &   \\\\   \n",
      "                                         & (0.0598)       &                & (0.0626)       &   \\\\   \n",
      "   Vote Share                            & -0.8972        & -0.8886        & 0.1393         & 0.1673\\\\   \n",
      "                                         & (0.6194)       & (0.6179)       & (0.7442)       & (0.7428)\\\\   \n",
      "   Men's Salience $\\times$ Women Rep.    & 0.0681$^{*}$   &                & 0.0154         &   \\\\   \n",
      "                                         & (0.0382)       &                & (0.0394)       &   \\\\   \n",
      "   Women's Salience                      &                & 0.3817$^{***}$ &                & 0.3676$^{***}$\\\\   \n",
      "                                         &                & (0.0566)       &                & (0.0530)\\\\   \n",
      "   Women's Salience $\\times$ Women Rep.  &                & 0.1106$^{**}$  &                & 0.0786$^{**}$\\\\   \n",
      "                                         &                & (0.0444)       &                & (0.0384)\\\\   \n",
      "   \\midrule\n",
      "   \\emph{Fixed-effects}\\\\\n",
      "   Legislator                            & Yes            & Yes            & Yes            & Yes\\\\  \n",
      "   Time                                  & Yes            & Yes            & Yes            & Yes\\\\  \n",
      "   Issue                                 & Yes            & Yes            & Yes            & Yes\\\\  \n",
      "   Party                                 &                & Yes            &                & Yes\\\\  \n",
      "   \\midrule\n",
      "   \\emph{Fit statistics}\\\\\n",
      "   Observations                          & 545,848        & 545,848        & 744,425        & 744,425\\\\  \n",
      "   Squared Correlation                   & 0.29751        & 0.29927        & 0.24097        & 0.23696\\\\  \n",
      "   Pseudo R$^2$                          & 0.28421        & 0.28527        & 0.28147        & 0.28002\\\\  \n",
      "   BIC                                   & 1,904,698.4    & 1,901,906.2    & 2,061,182.1    & 2,065,467.1\\\\  \n",
      "   \\midrule \\midrule\n",
      "   \\multicolumn{5}{l}{\\emph{Clustered (Legislator \\& Time) standard-errors in parentheses}}\\\\\n",
      "   \\multicolumn{5}{l}{\\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\\\\n",
      "\\end{tabular}\n",
      "\\par\\endgroup\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args='Rscript --vanilla ../code/poisson_estimation.R', returncode=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run R script from the command line\n",
    "subprocess.run(\"Rscript --vanilla ../code/poisson_estimation.R\", shell=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Materials D\n",
    "\n",
    "## Figure A1: Confusion Matrix "
   ]
  },
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   "execution_count": 12,
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        },
        "title": {
         "font": {
          "color": "black",
          "family": "Arial",
          "size": 26
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         "text": "Confusion Matrix: Model Predictions vs. Annotated Labels"
        },
        "width": 800,
        "xaxis": {
         "anchor": "y",
         "constrain": "domain",
         "domain": [
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   ],
   "source": [
    "print_confusion_matrix(config.confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full combined public opinion data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>issue</th>\n",
       "      <th>priority</th>\n",
       "      <th>gender</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>education</td>\n",
       "      <td>0.04</td>\n",
       "      <td>male</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-21</td>\n",
       "      <td>education</td>\n",
       "      <td>0.02</td>\n",
       "      <td>male</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-28</td>\n",
       "      <td>education</td>\n",
       "      <td>0.06</td>\n",
       "      <td>male</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-26</td>\n",
       "      <td>education</td>\n",
       "      <td>0.08</td>\n",
       "      <td>male</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-08-02</td>\n",
       "      <td>education</td>\n",
       "      <td>0.03</td>\n",
       "      <td>male</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19563</th>\n",
       "      <td>2023-12-11</td>\n",
       "      <td>defense</td>\n",
       "      <td>0.06</td>\n",
       "      <td>female</td>\n",
       "      <td>uk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19564</th>\n",
       "      <td>2023-12-18</td>\n",
       "      <td>defense</td>\n",
       "      <td>0.07</td>\n",
       "      <td>female</td>\n",
       "      <td>uk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19565</th>\n",
       "      <td>2023-12-24</td>\n",
       "      <td>defense</td>\n",
       "      <td>0.07</td>\n",
       "      <td>female</td>\n",
       "      <td>uk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19566</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>defense</td>\n",
       "      <td>0.07</td>\n",
       "      <td>female</td>\n",
       "      <td>uk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19567</th>\n",
       "      <td>2024-01-07</td>\n",
       "      <td>defense</td>\n",
       "      <td>0.07</td>\n",
       "      <td>female</td>\n",
       "      <td>uk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19568 rows × 5 columns</p>\n",
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      ],
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       "             date      issue priority  gender country\n",
       "0      2009-01-20  education     0.04    male      us\n",
       "1      2009-06-21  education     0.02    male      us\n",
       "2      2009-06-28  education     0.06    male      us\n",
       "3      2009-07-26  education     0.08    male      us\n",
       "4      2009-08-02  education     0.03    male      us\n",
       "...           ...        ...      ...     ...     ...\n",
       "19563  2023-12-11    defense     0.06  female      uk\n",
       "19564  2023-12-18    defense     0.07  female      uk\n",
       "19565  2023-12-24    defense     0.07  female      uk\n",
       "19566  2024-01-01    defense     0.07  female      uk\n",
       "19567  2024-01-07    defense     0.07  female      uk\n",
       "\n",
       "[19568 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return full combined dataset \n",
    "return_mip_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive statistics for public opinion data (Appendix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllllll}\n",
      "\\toprule\n",
      "Country & Issue & Gender & count & mean & std & min & 25% & 50% & 75% & max \\\\\n",
      "\\midrule\n",
      "UK & Crime & female & 182.00 & 0.19 & 0.07 & 0.07 & 0.14 & 0.20 & 0.24 & 0.39 \\\\\n",
      "UK & Crime & male & 182.00 & 0.19 & 0.06 & 0.09 & 0.14 & 0.19 & 0.23 & 0.38 \\\\\n",
      "UK & Defense & female & 182.00 & 0.09 & 0.04 & 0.03 & 0.06 & 0.08 & 0.12 & 0.29 \\\\\n",
      "UK & Defense & male & 182.00 & 0.11 & 0.04 & 0.05 & 0.08 & 0.10 & 0.13 & 0.25 \\\\\n",
      "UK & Economy & female & 182.00 & 0.36 & 0.15 & 0.18 & 0.22 & 0.32 & 0.53 & 0.61 \\\\\n",
      "UK & Economy & male & 182.00 & 0.42 & 0.12 & 0.27 & 0.31 & 0.40 & 0.54 & 0.65 \\\\\n",
      "UK & Education & female & 182.00 & 0.16 & 0.04 & 0.10 & 0.13 & 0.15 & 0.17 & 0.32 \\\\\n",
      "UK & Education & male & 182.00 & 0.12 & 0.03 & 0.06 & 0.10 & 0.11 & 0.13 & 0.24 \\\\\n",
      "UK & Environment & female & 182.00 & 0.24 & 0.08 & 0.10 & 0.18 & 0.25 & 0.30 & 0.42 \\\\\n",
      "UK & Environment & male & 182.00 & 0.23 & 0.07 & 0.08 & 0.18 & 0.24 & 0.28 & 0.38 \\\\\n",
      "UK & Health & female & 182.00 & 0.53 & 0.11 & 0.32 & 0.44 & 0.55 & 0.61 & 0.81 \\\\\n",
      "UK & Health & male & 182.00 & 0.43 & 0.11 & 0.24 & 0.34 & 0.44 & 0.50 & 0.70 \\\\\n",
      "UK & Immigration & female & 182.00 & 0.24 & 0.06 & 0.09 & 0.20 & 0.23 & 0.29 & 0.41 \\\\\n",
      "UK & Immigration & male & 182.00 & 0.26 & 0.05 & 0.13 & 0.22 & 0.25 & 0.29 & 0.37 \\\\\n",
      "UK & International Affairs & female & 182.00 & 0.46 & 0.17 & 0.19 & 0.26 & 0.46 & 0.62 & 0.73 \\\\\n",
      "UK & International Affairs & male & 182.00 & 0.51 & 0.17 & 0.24 & 0.32 & 0.53 & 0.66 & 0.77 \\\\\n",
      "UK & Tax & female & 182.00 & 0.03 & 0.02 & 0.01 & 0.03 & 0.03 & 0.04 & 0.11 \\\\\n",
      "UK & Tax & male & 182.00 & 0.06 & 0.02 & 0.02 & 0.04 & 0.05 & 0.06 & 0.13 \\\\\n",
      "US & Crime & female & 205.00 & 0.06 & 0.02 & 0.02 & 0.05 & 0.06 & 0.08 & 0.14 \\\\\n",
      "US & Crime & male & 205.00 & 0.06 & 0.02 & 0.02 & 0.04 & 0.05 & 0.07 & 0.15 \\\\\n",
      "US & Defense & female & 205.00 & 0.07 & 0.02 & 0.03 & 0.06 & 0.07 & 0.08 & 0.13 \\\\\n",
      "US & Defense & male & 205.00 & 0.07 & 0.02 & 0.03 & 0.06 & 0.07 & 0.08 & 0.14 \\\\\n",
      "US & Economy & female & 205.00 & 0.14 & 0.04 & 0.08 & 0.11 & 0.13 & 0.16 & 0.25 \\\\\n",
      "US & Economy & male & 205.00 & 0.19 & 0.04 & 0.09 & 0.16 & 0.18 & 0.21 & 0.30 \\\\\n",
      "US & Education & female & 205.00 & 0.06 & 0.01 & 0.03 & 0.05 & 0.06 & 0.07 & 0.10 \\\\\n",
      "US & Education & male & 205.00 & 0.05 & 0.01 & 0.02 & 0.04 & 0.05 & 0.05 & 0.09 \\\\\n",
      "US & Environment & female & 205.00 & 0.11 & 0.03 & 0.05 & 0.09 & 0.11 & 0.13 & 0.21 \\\\\n",
      "US & Environment & male & 205.00 & 0.11 & 0.03 & 0.06 & 0.09 & 0.11 & 0.13 & 0.16 \\\\\n",
      "US & Health & female & 205.00 & 0.24 & 0.04 & 0.13 & 0.22 & 0.24 & 0.26 & 0.34 \\\\\n",
      "US & Health & male & 205.00 & 0.18 & 0.03 & 0.11 & 0.16 & 0.18 & 0.20 & 0.29 \\\\\n",
      "US & Immigration & female & 205.00 & 0.09 & 0.03 & 0.03 & 0.06 & 0.09 & 0.12 & 0.16 \\\\\n",
      "US & Immigration & male & 205.00 & 0.12 & 0.04 & 0.03 & 0.09 & 0.12 & 0.15 & 0.22 \\\\\n",
      "US & Tax & female & 205.00 & 0.04 & 0.02 & 0.01 & 0.02 & 0.04 & 0.06 & 0.11 \\\\\n",
      "US & Tax & male & 205.00 & 0.06 & 0.04 & 0.01 & 0.03 & 0.04 & 0.09 & 0.13 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_MIP_descriptive_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Public Opinion Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "type": "scattergl",
         "x": [
          "2017-12-26T00:00:00",
          "2018-01-02T00:00:00",
          "2018-01-09T00:00:00",
          "2018-01-16T00:00:00",
          "2018-01-23T00:00:00",
          "2018-01-30T00:00:00",
          "2018-02-06T00:00:00",
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          "2018-02-20T00:00:00",
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         "anchor": "y3",
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         "matches": "x",
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         "showticklabels": false,
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         "matches": "x",
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        "xaxis8": {
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         "matches": "x",
         "showticklabels": false,
         "tickangle": 45
        },
        "yaxis": {
         "anchor": "x",
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          0,
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         "showticklabels": true,
         "tickformat": ",.0%",
         "title": {
          "text": "Salience"
         }
        },
        "yaxis2": {
         "anchor": "x2",
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         "showticklabels": true,
         "tickformat": ",.0%"
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        "yaxis3": {
         "anchor": "x3",
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         "showticklabels": true,
         "tickformat": ",.0%",
         "title": {
          "text": "Salience"
         }
        },
        "yaxis4": {
         "anchor": "x4",
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         "showticklabels": true,
         "tickformat": ",.0%"
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        "yaxis5": {
         "anchor": "x5",
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         "showticklabels": true,
         "tickformat": ",.0%",
         "title": {
          "text": "Salience"
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        },
        "yaxis6": {
         "anchor": "x6",
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         "showticklabels": true,
         "tickformat": ",.0%"
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        "yaxis7": {
         "anchor": "x7",
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         "showticklabels": true,
         "tickformat": ",.0%",
         "title": {
          "text": "Salience"
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        },
        "yaxis8": {
         "anchor": "x8",
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         "showticklabels": true,
         "tickformat": ",.0%"
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       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot US \n",
    "\n",
    "make_mip_plot(mip = return_mip_data(), \n",
    "              col_wrap=2,\n",
    "              country = 'us')\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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        },
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        },
        "yaxis5": {
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         "showticklabels": true,
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        },
        "yaxis8": {
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# plot UK \n",
    "\n",
    "make_mip_plot(mip = return_mip_data(), \n",
    "              col_wrap=3,\n",
    "              country = 'uk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Legislators' attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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   "source": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vector Autorregressions "
   ]
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   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data \n",
    "\n",
    "path = '../data/pooled_data.csv'\n",
    "\n",
    "us = create_TS_data(path, 'us')\n",
    "uk = create_TS_data(path, 'uk')"
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   "source": [
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   "execution_count": 20,
   "metadata": {},
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    "# create VAR models and IRFs\n",
    "\n",
    "us_model, us_irf = create_VAR_model(data = us,\n",
    "                            lags = 10,\n",
    "                            irf_horizon = 4)\n",
    "\n",
    "uk_model, uk_irf = create_VAR_model(data = uk,\n",
    "                            lags=10,\n",
    "                            irf_horizon = 4)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot IRFs using VAR models"
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   "execution_count": 21,
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      "(0.001, 2.996)\n",
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      "(0.0, 3.304)\n",
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   "source": [
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    "\n",
    "plot_irf(uk_model, \n",
    "         uk_irf, \n",
    "         estimate = 'cumulative_effects')"
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        "yaxis6": {
         "anchor": "x6",
         "domain": [
          0.3833333333333333,
          0.6166666666666666
         ],
         "matches": "y5",
         "showticklabels": false
        },
        "yaxis7": {
         "anchor": "x7",
         "domain": [
          0.3833333333333333,
          0.6166666666666666
         ],
         "matches": "y5",
         "showticklabels": false
        },
        "yaxis8": {
         "anchor": "x8",
         "domain": [
          0.3833333333333333,
          0.6166666666666666
         ],
         "matches": "y5",
         "showticklabels": false
        },
        "yaxis9": {
         "anchor": "x9",
         "domain": [
          0,
          0.2333333333333333
         ]
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_irf(uk_model, \n",
    "         uk_irf, \n",
    "         estimate = 'cumulative_effects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive statistics for Twitter data (Appendix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llllllllll}\n",
      "\\toprule\n",
      "Country & Issue & count & mean & std & min & 25% & 50% & 75% & max \\\\\n",
      "\\midrule\n",
      "US & Crime & 204.00 & 37.43 & 42.55 & 0.00 & 11.00 & 22.00 & 48.00 & 283.00 \\\\\n",
      "US & Defense & 204.00 & 73.60 & 79.22 & 6.00 & 33.00 & 56.00 & 86.50 & 700.00 \\\\\n",
      "US & Economy & 204.00 & 174.05 & 127.83 & 2.00 & 74.50 & 142.50 & 251.75 & 744.00 \\\\\n",
      "US & Education & 204.00 & 60.01 & 38.11 & 0.00 & 35.00 & 53.00 & 77.00 & 276.00 \\\\\n",
      "US & Environment & 204.00 & 78.36 & 54.90 & 1.00 & 36.00 & 70.50 & 107.00 & 359.00 \\\\\n",
      "US & Health & 204.00 & 143.90 & 129.05 & 0.00 & 44.00 & 101.50 & 230.00 & 872.00 \\\\\n",
      "US & Immigration & 204.00 & 42.45 & 51.37 & 0.00 & 11.00 & 25.00 & 50.00 & 333.00 \\\\\n",
      "US & International_Affairs & 204.00 & 49.21 & 43.21 & 0.00 & 19.75 & 36.00 & 66.00 & 230.00 \\\\\n",
      "US & Tax & 204.00 & 19.25 & 21.13 & 0.00 & 5.00 & 13.00 & 27.00 & 170.00 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_twitter_descriptive_stats(country = 'us')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llllllllll}\n",
      "\\toprule\n",
      "Country & Issue & count & mean & std & min & 25% & 50% & 75% & max \\\\\n",
      "\\midrule\n",
      "UK & Crime & 182.00 & 20.86 & 23.25 & 1.00 & 10.00 & 15.00 & 24.00 & 250.00 \\\\\n",
      "UK & Defense & 182.00 & 23.00 & 21.57 & 2.00 & 11.00 & 17.00 & 26.00 & 143.00 \\\\\n",
      "UK & Economy & 182.00 & 99.98 & 67.71 & 16.00 & 53.00 & 77.00 & 133.50 & 380.00 \\\\\n",
      "UK & Education & 182.00 & 49.72 & 45.41 & 4.00 & 25.00 & 37.00 & 61.00 & 415.00 \\\\\n",
      "UK & Environment & 182.00 & 39.09 & 27.02 & 5.00 & 19.00 & 33.00 & 51.75 & 151.00 \\\\\n",
      "UK & Health & 182.00 & 95.21 & 95.60 & 7.00 & 27.25 & 57.50 & 132.75 & 540.00 \\\\\n",
      "UK & Immigration & 182.00 & 16.15 & 13.17 & 0.00 & 9.00 & 13.50 & 21.00 & 120.00 \\\\\n",
      "UK & International_Affairs & 182.00 & 66.24 & 39.79 & 8.00 & 35.00 & 55.50 & 89.00 & 215.00 \\\\\n",
      "UK & Tax & 182.00 & 4.20 & 6.35 & 0.00 & 1.00 & 2.00 & 4.75 & 46.00 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_twitter_descriptive_stats(country = 'uk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Granger Causality Tables in Appendix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrl}\n",
      "\\toprule\n",
      "Coefficient & Test statistic & p-value & Critical value & df \\\\\n",
      "\\midrule\n",
      "Men Reps' Attention -> Men Reps' Attention & 17.160000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Men Reps' Attention -> Women Reps' Attention & 2.940000 & 0.001000 & 1.832000 & (10, 6264) \\\\\n",
      "Men Reps' Attention -> Women's Salience & 2.981000 & 0.001000 & 1.832000 & (10, 6264) \\\\\n",
      "Men Reps' Attention -> Men's Salience & 2.202000 & 0.015000 & 1.832000 & (10, 6264) \\\\\n",
      "Women Reps' Attention -> Men Reps' Attention & 7.180000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Women Reps' Attention -> Women Reps' Attention & 7.639000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Women Reps' Attention -> Women's Salience & 2.827000 & 0.002000 & 1.832000 & (10, 6264) \\\\\n",
      "Women Reps' Attention -> Men's Salience & 4.010000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Women's Salience -> Men Reps' Attention & 6.892000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Women's Salience -> Women Reps' Attention & 6.086000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Women's Salience -> Women's Salience & 10.690000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Women's Salience -> Men's Salience & 9.286000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Men's Salience -> Men Reps' Attention & 7.049000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Men's Salience -> Women Reps' Attention & 4.542000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Men's Salience -> Women's Salience & 6.329000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "Men's Salience -> Men's Salience & 10.380000 & 0.000000 & 1.832000 & (10, 6264) \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print granger table for appendix for US model \n",
    "\n",
    "granger_table(us_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrl}\n",
      "\\toprule\n",
      "Coefficient & Test statistic & p-value & Critical value & df \\\\\n",
      "\\midrule\n",
      "Men Reps' Attention -> Men Reps' Attention & 4.360000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men Reps' Attention -> Women Reps' Attention & 3.328000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men Reps' Attention -> Women's Salience & 4.414000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men Reps' Attention -> Men's Salience & 4.152000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Women Reps' Attention -> Men Reps' Attention & 2.996000 & 0.001000 & 1.881000 & (9, 6020) \\\\\n",
      "Women Reps' Attention -> Women Reps' Attention & 2.459000 & 0.009000 & 1.881000 & (9, 6020) \\\\\n",
      "Women Reps' Attention -> Women's Salience & 1.189000 & 0.297000 & 1.881000 & (9, 6020) \\\\\n",
      "Women Reps' Attention -> Men's Salience & 3.304000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Women's Salience -> Men Reps' Attention & 5.471000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Women's Salience -> Women Reps' Attention & 6.408000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Women's Salience -> Women's Salience & 4.731000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Women's Salience -> Men's Salience & 10.760000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men's Salience -> Men Reps' Attention & 7.996000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men's Salience -> Women Reps' Attention & 5.381000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men's Salience -> Women's Salience & 4.137000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "Men's Salience -> Men's Salience & 10.530000 & 0.000000 & 1.881000 & (9, 6020) \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print granger table for appendix for US model \n",
    "\n",
    "granger_table(uk_model)"
   ]
  }
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